A NEURAL-BASED MODEL PREDICTIVE CONTROL TO TACKLE STEERING DELAY OF THE IARA AUTONOMOUS CAR

Name: Rânik Guidolini
Type: MSc dissertation
Publication date: 04/09/2017
Advisor:

Namesort descending Role
Claudine Santos Badue Gonçalves Advisor *

Examining board:

Namesort descending Role
Claudine Santos Badue Gonçalves Advisor *
Denis Fernando Wolf External Examiner *
Thiago Oliveira dos Santos Internal Examiner *

Summary: In this work, we propose a Neural Based Model Predictive Control (N-MPC) approach to tackle delays in the steering plant of autonomous cars. We examined the N-MPC approach as an alternative for the implementation of the Intelligent and Autonomous Robotic Automobile (IARA) steering control subsystem. For that, we compared the standard solution, based on the Proportional Integral Derivative (PID) control approach, with the N-MPC approach. The PID steering control subsystem works well in IARA for speeds of up to 25 km/h. However, above this speed, IARA’s Steering Plant delays are too high to allow proper operation with a PID approach. We tried and modeled the IARA’s Steering Plant using a neural network and employed this neural model in the N-MPC approach. The N-MPC approach outperformed the PID approach by reducing the impact of IARA’s Steering Plant delays and allowing the autonomous operation of IARA at speeds of up to 37 km/h – an increase of 48% in the maximum stable speed.

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